环境与遗传交互作用对新疆不同职业人群睡眠质量影响的研究

发布时间：2018-06-15 02:28

  本文选题：新疆不同职业人群 + 职业紧张　； 参考：《新疆医科大学》2017年博士论文

【摘要】：目的:调查新疆不同职业人群职业紧张、睡眠质量状况及其影响因素,并分析两者之间的关系,研究神经递质对新疆不同职业人群职业紧张和睡眠质量的影响。探讨DRD2、NET、COMT、GABRA、5-HTR2A和5-HTTLPR基因多态性与睡眠质量的关系,比较不同睡眠质量组以及不同紧张程度组间5-HTT基因启动子区DNA甲基化的差异,探讨表观遗传学在职业紧张与睡眠质量相关性中的作用。综合分析基因-基因、基因-环境(职业紧张)交互作用对新疆不同职业人群睡眠质量的影响。完善新疆不同职业人群职业紧张和睡眠质量的流行病学资料,为制定和提高新疆不同职业人群的睡眠质量提供科学依据。方法:1)本研究采取多阶段抽样方法,使用职业紧张量表、付出-回报失衡量表、匹兹堡睡眠质量量表,对2,650名新疆不同职业人群进行现况调查。2)随机抽取240人作为神经递质的测定对象,利用ELISA法检测血浆5-HT、NE、E、DA、GABA水平,利用结构方程模型分析职业紧张、神经递质以及睡眠质量之间的关系。3)分层随机抽取720名研究对象,应用SNaPshot技术检测多巴胺D2受体(rs1799732、rs1800497)、去甲肾上腺素转运体(rs2242446、rs5569)、γ-氨基丁酸A型受体(rs3219151、rs2279020)、儿茶酚-O-甲基转移酶(rs4680)、五羟色胺2A受体(rs6311、rs6313)和5-HTTLPR基因10个位点的多态性,比较不同睡眠质量组的基因型分布,了解影响睡眠质量的易感基因。4)采用MethylTarget技术检测五羟色胺转运体(5-HTT)启动子区DNA甲基化程度。5)应用SHEsis软件进行单体型分析,广义多因子降维法(GMDR)构建基因-基因、基因-环境交互作用对睡眠质量影响的最佳模型。应用多元Logistic回归分析方法进行基因-基因、基因-环境的交互作用危险性分析,计算交互作用风险的比值比(OR)及其95%可信区间(CI)。结果:1)本次研究共发放调查问卷2,650份,收回有效合格问卷2,400份,问卷有效率为90.6%。2)本次调查的新疆不同职业人群职业紧张得分中小学教师、野外油田工人、铁路职工高于大学教师、行政管理人员和银行职员(P0.05);工龄10~20年者职业任务得分高于工龄20年者(P0.05);男性职业任务、个体紧张反应得分高于女性(P0.05);少数民族职业任务和个体紧张反应得分高于汉族(P0.05);硕士及以上学历者职业任务得分高于本科和大中专以下学历者(p0.05);吸烟者职业任务和个体紧张反应得分高于未吸烟者(p0.05);饮酒者职业任务得分高于未饮酒者(p0.05)。3)不同工种外在付出、内在投入和工作回报得分有差异(p0.05),其中野外油田工人、中小学教师和铁路职工付出-回报不平衡;不同工龄外在付出、内在投入和工作回报得分有差异(p0.05);不同性别工作回报、内在投入得分差异有统计学意义(p0.05),其中工作回报得分女性高于男性(p0.05),内在投入得分男性高于女性(p0.05),提示男性付出-回报不平衡;不同文化程度外在付出、工作回报和内在投入得分差异有统计学意义(p0.05),其中硕士及以上学历者付出-回报不平衡;不同吸烟状况工作回报和内在投入得分差异有统计学意义(p0.05),其中吸烟者付出-回报不平衡。4)新疆不同职业人群psqi总分为(4.92±3.15)分,以psqi总分5分作为判断睡眠质量问题的标准,新疆不同职业人群睡眠质量问题发生率为36.4%;大学教师、野外油田工人和铁路职工睡眠质量得分高于中小学教师、行政管理人员和银行员工(p0.05);工龄20年和工龄10~20年者睡眠质量得分高于工龄≤10年者(p0.05),随着工龄的增加,研究对象的睡眠质量随之下降;女性睡眠质量得分高于男性(p0.05),离异或丧偶者睡眠质量得分高于未婚者(p0.05);吸烟者睡眠质量得分高于不吸烟者(p0.05)。5)不同紧张强度组在psqi总分、主观睡眠质量、睡眠时间、睡眠障碍、催眠药物和日间功能障碍上得分差异有统计学意义(p0.05);其中高度紧张强度组psqi总分、睡眠时间、睡眠障碍和日间功能障碍高于中度紧张组和低度紧张组(p0.05),高度紧张组催眠药物得分高于低度紧张组(p0.05),中度紧张组主观睡眠质量得分高于低度紧张组(p0.05),随着紧张程度的增高,睡眠质量越差。不同付出-回报失衡组在psqi总分、入睡时间、睡眠时间、睡眠障碍、催眠药物和日间功能障碍上得分差异有统计学意义(p0.05),高付出-低回报组得分均高于低付出-高回报组(p0.05),高付出-低回报组睡眠质量较差。6)高度紧张组血浆5-ht、ne水平高于中、低度职业紧张组(p0.05);高付出-低回报组血浆ne水平高于低付出-高回报组(p0.05)。偏相关分析发现,职业任务和个体紧张反应与5-ht和ne呈正相关关系(p0.05),个体应对资源得分与da呈负相关关系(p0.05);外在付出与ne呈正相关关系(p0.05),内在投入与5-ht呈正相关关系(p0.05);个体紧张反应和外在付出均与gaba水平呈负相关关系(p0.05)。睡眠质量问题组的5-ht、da和e水平高于非睡眠质量问题组(p0.05)。偏相关分析发现,5-ht、ne、da和e水平与睡眠质量部分因子呈正相关关系(p0.05),gaba水平与睡眠时间呈负相关关系(p0.05)。7)多元logistic回归分析发现影响睡眠质量的因素为:工种、个体紧张反应、外在付出、工作回报、内在投入、5-ht、ne和da;通过对职业紧张-生理紧张反应(神经递质)-睡眠质量之间复杂关联的结构方程模型发现,职业紧张、付出-回报失衡、生理紧张反应对睡眠质量有直接影响,职业紧张对生理紧张反应有直接影响,生理紧张反应是职业紧张对睡眠质量影响的中介因素。8)多巴胺d2受体(drd2)基因、去甲肾上腺素转运体(net)基因、γ-氨基丁酸a型受体(gabra)基因、儿茶酚-o-甲基转移酶(comt)基因、五羟色胺2a受体(5-htr2a)基因和5-httlpr基因的10个tagsnps位点不同睡眠质量组基因型分布均符合hardy-weinberg平衡定律。9)睡眠质量遗传易感性分析:?drd2受体基因rs1800497位点a1a1基因型入睡时间、睡眠时间、睡眠障碍和催眠药物得分均高于a2a2基因型组(p0.05);a1a1基因型(or=1.938,95%ci:1.228-3.058)是睡眠质量问题的易感基因型;?net基因rs5569位点aa基因型主观睡眠质量和睡眠效率得分高于gg基因型(p0.05);ag基因型(or=1.569,95%ci:1.109-2.220)和aa基因型(or=2.231,95%ci:1.353-3.678)是睡眠质量问题的易感基因型;?gabra1基因rs2279020位点g等位基因突变频率为64.9%;gg基因型主观睡眠质量和睡眠效率得分高于aa基因型和ag基因型(p0.05);gg基因型(or=1.183,95%ci:1.064-2.015)是睡眠质量问题的易感基因型;?5-htr2a基因rs6311位点gg基因型主观睡眠质量和日间功能障碍得分均高于aa基因型组和ag基因型组(p0.05),r6313位点cc基因型主观睡眠质量和睡眠效率得分高于tt基因型(p0.05);rs6311位点的gg基因型(or=1.929,95%ci:1.222-3.047)和rs6313位点cc基因型(or=3.344,95%ci:2.062-5.422)是睡眠质量问题的易感基因型;?5-httlpr基因ss基因型主观睡眠质量得分高于ll基因型组(p0.05);ss基因型(or=2.118,95%ci:1.053-4.257)是睡眠质量问题的易感基因型;?comt基因rs4680位点不同基因型组间睡眠质量得分没有差异(p0.05);logistic回归显示rs4680位点多态性对睡眠质量没有影响。10)高职业紧张组5-htt基因启动子区cpg2、cpg4、cpg6、cpg8、cpg12、cpg13、cpg16、cpg17、cpg47和cpg58位点甲基化水平低于低职业紧张组(p0.05);睡眠质量问题组5-htt基因启动子区cpg5、cpg8、cpg13、cpg14、cpg16、cpg30、cpg34和cpg47位点甲基化水平低于非睡眠质量问题组(p0.05);睡眠质量问题组中高度职业紧张组cpg13、cpg16、cpg34和cpg47位点甲基化水平低于低度职业紧张组(p0.05)。11)通过分析单倍体型与睡眠质量问题易感性的关系发现drd2基因rs1799732位点和rs1800497位点的i-a1为睡眠质量的危险单倍型,i-a2为保护单倍型;net基因rs2242446位点和rs5569位点的t-a为睡眠质量的危险单倍型,t-g为保护单倍型;5-htr2a基因rs6313和rs6311位点的g-c为睡眠质量危险单倍型,g-t为保护单倍型;gabra基因rs2279020位点和rs3219151位点的a-c为睡眠质量保护单倍型,g-c为危险单倍型。12)采用gmdr软件分析,rs1800497、rs2279020和rs6313位点组成的交互模型是所生成的基因-基因交互模型中的最佳模型。进一步的logistic回归分析发现,drd2基因rs1800497位点、grbra1基因rs2279020位点和5-htr2a基因rs6313位点的交互作用能增加新疆不同职业人群睡眠质量问题的患病风险(OR=1.186,95%CI:1.015-3.432);rs6313、职业紧张和ERI组成的交互作用模型为最佳基因-环境交互作用模型,进一步的Logistic回归分析发现,三者之间的交互作用能使新疆不同职业人群睡眠质量问题的患病风险增加(OR=1.171,95%CI:1.012-1.355)。结论:1)男性、少数民族、工龄10~20年、已婚、吸烟或饮酒的中小学教师、野外油田工人、铁路职工职业紧张得分较高。男性、高学历的野外油田工人、中小学教师和铁路职工付出-回报不平衡。2)研究人群PSQI总分和睡眠质量问题患病率较高,说明睡眠质量较差。3)随着紧张程度的增加,睡眠质量随之下降。付出-回报失衡越重,睡眠质量越差。4)神经递质对职业紧张和睡眠质量产生影响;工种(铁路职员、野外油田工人)、个体紧张反应、外在付出、内在投入、5-HT、NE和DA是睡眠质量的危险因素,而工作回报是睡眠质量问题的保护因素;职业紧张、付出-回报失衡、生理紧张反对睡眠质量有直接影响,职业紧张对生理紧张反应有直接影响,生理紧张反应是职业紧张对睡眠质量影响的中介因素。5)DRD2受体基因rs1800497位点A1A1基因型、NET基因rs5569位点AA基因型、GABRA1基因rs2279020位点GG基因型、5-HTR2A基因rs6311位点GG基因型和r6313位点CC基因型以及5-HTTLPR基因SS基因型是睡眠质量问题的易感基因型。6)5-HTT基因启动子区DNA甲基化水平降低,可增加职业紧张和睡眠质量问题的风险,5-HTT基因启动子区DNA甲基化水平在职业紧张与睡眠质量相关性中有一定的调控作用。7)DRD2基因、NET基因、GABRA基因和5-HTR2A基因tag SNPs位点可以通过单倍型的联合影响睡眠质量。8)DRD2基因rs1800497位点、GRBRA1基因rs2279020位点和5-HTR2A基因rs6313位点的基因-基因交互作用和rs6313、职业紧张和ERI组成的环境-基因交互作用能增加新疆不同职业人群睡眠质量问题的患病风险。
[Abstract]:Objective: To investigate the occupational stress, sleep quality and its influencing factors in different occupational groups in Xinjiang, and to analyze the relationship between the two and study the effects of neurotransmitters on occupational stress and sleep quality in different occupational groups in Xinjiang. The relationship between the polymorphism of DRD2, NET, COMT, GABRA, 5-HTR2A and 5-HTTLPR and the quality of sleep is discussed and the different sleep is compared. The difference of DNA methylation in the 5-HTT gene promoter region between the sleep quality group and the different tension groups, and to explore the role of epigenetics in the relationship between occupational stress and sleep quality. The effect of gene gene, gene environment (occupational stress) interaction on the sleep quality of different occupational groups in Xinjiang. To improve the different occupations in Xinjiang. The epidemiological data of occupational stress and quality of sleep in the population provided a scientific basis for making and improving the quality of sleep in different occupational groups in Xinjiang. Methods: 1) a multi stage sampling method was adopted in this study, using the occupational stress scale, the pay return imbalance scale, the Pittsburgh sleep quality scale, and the 2650 different occupational groups in Xinjiang. 240 people were randomly selected as the target of neurotransmitters, and the plasma 5-HT, NE, E, DA, GABA levels were detected by ELISA, and the structural equation model was used to analyze occupational stress, neurotransmitters and the relationship between sleep quality and.3) by stratified random extraction of 720 research subjects and using SNaPshot technique to detect dopamine D2 receptor (rs1799732,) Rs1800497), the norepinephrine transporter (rs2242446, rs5569), gamma aminobutyric acid A receptor (rs3219151, rs2279020), catechol -O- methyltransferase (rs4680), five serotonin 2A receptor (rs6311, rs6313) and 5-HTTLPR gene polymorphisms, compared with the genotype distribution of different sleep quality groups, understand the susceptible basis of sleep quality. .4) using MethylTarget technology to detect DNA methylation degree in the promoter region of five serotonin transporter (5-HTT).5) using SHEsis software for haplotype analysis and generalized multiple factor reduction (GMDR) construction of gene gene gene, gene environment interaction on the best model of sleep quality. Multiple Logistic regression analysis was used to carry out genes. - gene, gene - environment interaction risk analysis, the ratio Ratio ratio (OR) and 95% confidence interval (CI) of the interaction risk (CI). Results: 2650 questionnaires were issued in this study, 2400 valid questionnaires were collected, and the efficiency of the questionnaire was 90.6%.2) in this survey, the occupational stress scores in different occupational groups in Xinjiang Teachers, field workers, railway workers were higher than university teachers, administrators and bank staff (P0.05); the career tasks of 10~20 years were higher than those of 20 years (P0.05); male occupational tasks, individual tension response scores were higher than women (P0.05); ethnic minority occupational tasks and individual tension response scores were higher than those of the Han (P0.05); master's degree (P0.05); The professional task scores of those with or above were higher than those of the undergraduate and junior secondary school (P0.05); the scores of occupational tasks and individual stressful responses of smokers were higher than those of non smokers (P0.05); the professional task scores of drinkers were higher than those of non drinkers (P0.05).3) and the internal input and work return score were different (P0.05), among them, the field was different (P0.05). Oil field workers, primary and middle school teachers and railway workers pay unbalance; the external pay, internal investment and work return score differ (P0.05); the difference in the internal input score is statistically significant (P0.05), among which the score of work returns is higher than that of men (P0.05), and the inner input is higher than that of the female. Sex (P0.05) showed that male pay and return imbalances; the difference of external pay, work return and internal input were statistically significant (P0.05), in which the payback and return were not balanced; the difference of work return and internal investment in different smoking conditions was statistically significant (P0.05), in which smokers paid. - return imbalanced.4) the total score of PSQI in different occupational groups in Xinjiang was (4.92 + 3.15), and the total score of PSQI was 5 points as the standard for judging the quality of sleep. The incidence of sleep quality of different occupational groups in Xinjiang was 36.4%; university teachers, field workers and railway workers were higher than primary and secondary school teachers, administrators and silver The sleep quality scores of 20 years and 10~20 years were higher than those of less than 10 years (P0.05). The sleep quality of the subjects decreased with the increase of working age; the sleep quality scores of women were higher than those of men (P0.05), and the sleep quality scores of divorced or widowed persons were higher than those of unmarried men (P0.05), and the sleep quality scores of smokers were higher than those of the unmarried people (P0.05). Smokers (P0.05).5) in the PSQI total score of different intensity groups, the scores of subjective sleep quality, sleep time, sleep disorder, hypnotic drugs and daytime dysfunction were statistically significant (P0.05), and the total score of PSQI, sleep time, sleep disorder and daytime dysfunction in the high tension group were higher than those in the moderate stress group and the low tension group (P0 .05), in the high tension group, the scores of hypnotic drugs were higher than the low tension group (P0.05). The subjective sleep quality score of the moderate stress group was higher than the low tension group (P0.05), the worse the sleep quality was with the increase of tension. The different pay return imbalance group was in the PSQI total, sleep time, sleep disorder, hypnotic drug, and daytime dysfunction. The score difference was statistically significant (P0.05), the scores of the high pay and low return group were higher than the low pay group (P0.05), the high pay and low return group had poor sleep quality.6) the Plasma 5-HT, the ne level was higher than the low occupational stress group (P0.05), and the plasma NE level in the high pay low return group was higher than the low pay high return group (P0.05). Partial phase was higher than the low pay and high return group (P0.05). The relationship between occupational task and individual stress was positively correlated with 5-HT and ne (P0.05), and there was a negative correlation between individual coping resources and DA (P0.05); external pay was positively correlated with ne (P0.05), and intrinsic input was positively correlated with 5-HT (P0.05); individual tension response and external pay were negatively correlated with GABA (P). 0.05) the level of 5-HT, DA and E in the sleep quality group was higher than that of the non sleep quality group (P0.05). Partial correlation analysis found that the level of 5-HT, NE, DA and E had a positive correlation with the quality of sleep (P0.05), and the level of GABA was negatively correlated with the sleep time (P0.05).7) multiple regression analysis found that the factors affecting the quality of sleep were: work Species, individual stress response, external pay, work return, internal input, 5-HT, NE, and Da; through a structural equation model of the complex association between occupational stress physiological stress response (neurotransmitter) - sleep quality, occupational stress, pay return imbalance, physiological tension response directly affect the quality of sleep, and occupational stress to physiological stress The reaction has a direct effect. The physiological stress is the mediator of occupational stress on the quality of sleep.8) the dopamine D2 receptor (DRD2) gene, the norepinephrine transporter (net) gene, the gamma Aminobutyrate receptor (gabra) gene, the catechol -o- methyltransferase (COMT) gene, the five serotonin 2A receptor (5-HTR2A) gene and the 5-HTTLPR gene 10. The genotype distribution of different sleep quality groups in tagSNPs loci was consistent with the Hardy-Weinberg equilibrium law.9) genetic susceptibility analysis of sleep quality: the DRD2 receptor gene rs1800497 locus a1a1 genotypic sleep time, sleep time, sleep disorder and hypnotic drug score were higher than the a2a2 type group (P0.05); a1a1 genotype (or=1.938,95%ci:1.228-3.0). 58) is the susceptible genotype of sleep quality problems; the score of the subjective sleep quality and sleep efficiency of the rs5569 locus AA genotype of the net gene is higher than that of the GG genotype (P0.05), and the Ag genotype (or=1.569,95%ci:1.109-2.220) and AA genotype (or=2.231,95%ci:1.353-3.678) are the susceptible genotypes of the sleep mass problem; the rs2279020 locus of the GABRA1 gene is the allele of the G allele. Because the frequency of mutation was 64.9%, the score of subjective sleep quality and sleep efficiency of GG genotypes was higher than that of AA genotype and Ag genotype (P0.05), and GG genotype (or=1.183,95%ci:1.064-2.015) was a susceptible genotype of sleep quality problems, and the subjective sleep sleep quality and daytime dysfunction score of GG genotype of 5-HTR2A gene were higher than that of the AA genotype group at the 5-HTR2A gene rs6311 site. And the Ag genotype group (P0.05), the score of the subjective sleep quality and sleep efficiency of the r6313 locus CC genotype was higher than that of the TT genotype (P0.05), and the GG genotypes (or=1.929,95%ci:1.222-3.047) of the rs6311 locus and the CC genotype of the rs6313 loci (or=3.344,95%ci:2.062-5.422) were the susceptible genotypes of the sleep quality problems; The quality score was higher than that of the LL genotype group (P0.05); the SS genotype (or=2.118,95%ci:1.053-4.257) was a susceptible genotype of sleep quality problems; there was no difference in the sleep quality score between the rs4680 loci of the COMT gene (P0.05); logistic regression showed that the rs4680 locus polymorphism did not affect the quality of sleep in.10) and the 5-HTT base of the high occupational stress group Cpg2, cpg4, cpg6, cpg8, cpg12, cpg13, cpg16, cpg17, cpg47, and cpg58 methylation levels were lower than those of the low occupational stress group (P0.05). The level of methylation at cpg13, cpg16, cpg34, and cpg47 loci in the high occupational stress group was lower than the low occupational stress group (P0.05).11) by analyzing the relationship between the haplotype and the sleep quality problem, the DRD2 gene rs1799732 site and the rs1800497 locus i-a1 were the dangerous haplotypes of the sleep quality, and i-a2 was the protective haplotype; The T-A of the loci and rs5569 loci is a dangerous haplotype of sleep quality, T-G is the protection haplotype, and the G-C of the 5-HTR2A gene rs6313 and rs6311 loci is the haplotype of the sleep quality risk, G-T is the haplotype, the rs2279020 and rs3219151 loci of the gabra gene are the haplotype of the sleep quality protection. Analysis, the interaction model of the rs1800497, rs2279020 and rs6313 sites is the best model in the gene gene interaction model. Further logistic regression analysis found that the interinteraction of the DRD2 gene rs1800497 site, the grbra1 gene rs2279020 site and the 5-HTR2A gene rs6313 site could increase the sleep quality of different occupational populations in Xinjiang. The risk of the disease (OR=1.186,95%CI:1.015-3.432); the interaction model of rs6313, occupational stress and ERI was the best gene environment interaction model. Further Logistic regression analysis found that the interaction between the three groups could increase the risk of sleep quality problems in different occupational groups in Xinjiang (OR=1.171,95%CI:1. 012-1.355) conclusion: 1) male, ethnic minority, working age 10~20, married, smoking or drinking primary and secondary school teachers, field workers, railway workers have higher occupational stress scores. Male, high educated field workers, primary and secondary school teachers and railway workers pay off balance.2) study the total score of PSQI and the prevalence rate of sleep quality problems The higher, poor sleep quality.3) with the increase of tension, the quality of sleep decreased. The heavier the pay - return imbalance, the worse the quality of sleep.4); the neurotransmitters had an impact on occupational stress and quality of sleep; the types of work (railway staff, field workers), individual stress reactions, external pay, internal input, 5-HT, NE and DA were sleep quality. The risk factors of quantity, and work reward is the protective factor of sleep quality problems, occupational stress, pay return imbalance, physiological tension against sleep quality has direct influence, occupational stress has direct effect on physiological stress response, physiological tension reaction is the intermediary factor of occupational stress on sleep quality.5) DRD2 receptor gene rs1800497 position Point A1A1 genotypes, NET gene rs5569 locus AA genotypes, GABRA1 gene rs2279020 loci GG genotypes, 5-HTR2A gene rs6311 loci GG genotypes and r6313 loci CC genotypes and sleep quality problems are susceptible genotypes. There is a certain correlation between the DNA methylation level of 5-HTT gene promoter and the quality of sleep in the risk of sleep quality.
【学位授予单位】：新疆医科大学
【学位级别】：博士
【学位授予年份】：2017
【分类号】：R13
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本文编号：2020158